Elijah Willie

dioscRi improves transferability of deep learning models for cytometry data by integrating normalization, cell type hierarchies, and clinical outcome prediction.

PACE quantifies how proximity to different cell types affects gene expression in spatial transcriptomics data, revealing tumour-associated reprogramming in breast and melanoma tissues.

The paper identifies immune landscape differences in responders and non-responders to anti-PD-1 therapy in head and neck cancers using spatial mapping.

scMerge2 integrates large-scale multi-sample multi-condition single-cell data across cohorts, improving disease progression discrimination through cell-type expression signatures.

This study evaluates similarity metrics for clustering in situ imaging cytometry data and proposes FuseSOM, an ensemble algorithm that improves clustering performance.

This study uses machine learning on whole-genome sequencing data from multiple geographic regions to predict drug resistance in Mycobacterium tuberculosis, finding that performance varies across datasets due to geographical heterogeneity.